The natural variation of starch phosphate content in potatoes has been previously reported. It is known that, in contrast to raw starch, commercially phosphorylated starch is more stable at high temperatures and shear rates and has higher water capacity. The genetic improvement of phosphate content in potato starch by selection or engineering would allow the production of phosphorylated starch in a natural, environmentally friendly way without chemicals. The aim of the current research is to identify genomic SNPs associated with starch phosphorylation by carrying out a genome-wide association study in potatoes. A total of 90 S. tuberosum L. varieties were used for phenotyping and genotyping. The phosphorus content of starch in 90 potato cultivars was measured and then statistically analysed. Principal component analysis (PCA) revealed that the third and eighth principal components appeared to be sensitive to variation in phosphorus content (p = 0.0005 and p = 0.002, respectively). PC3 showed the correlation of starch phosphorus content with allelic variations responsible for higher phosphorylation levels, found in four varieties. Similarly, PC8 indicated that hybrid 785/8–5 carried an allele associated with high phosphorus content, while the Impala and Red Scarlet varieties carried alleles for low phosphorus content. Genotyping was carried out using an Illumina 22 K SNP potato array. A total of 15,214 scorable SNPs (71.7% success rate) was revealed. GWAS mapping plots were obtained using TASSEL based on several statistical models, including general linear models (GLMs), with and without accounting for population structure, as well as MLM. A total of 17 significant SNPs was identified for phosphorus content in potato starch, 14 of which are assigned to 8 genomic regions on chromosomes 1, 4, 5, 7, 8, 10, and 11. Most of the SNPs identified belong to protein coding regions; however, their allelic variation was not associated with changes in protein structure or function. A total of 8 novel genomic regions possibly associated with starch phosphorylation on potato chromosomes 1, 4, 5, 7, 8, 10, and 11 was revealed. Further validation of the SNPs identified and the analysis of the surrounding genomic regions for candidate genes will allow better understanding of starch phosphorylation biochemistry. The most indicative SNPs may be useful for developing diagnostic markers to accelerate the breeding of potatoes with predetermined levels of starch phosphorylation.

Nothing is currently known about microbial composition of saline lakes of the Novosibirsk region and its dependence on physical-chemical parameters of waters. We studied the structure of microbial communities of saline lakes of the Novosibirsk region and the effect of physical-chemical parameters of waters on microbial communities of these lakes. According to the ion content, the lakes were classified either as chloride or chloride-sulfate types. Water salinity ranges from 4.3 to 290 g L−1. Many diverse microbial communities were found. Filamentous and colonial Cyanobacteria of the genera Scytonema, Aphanocapsa, and/or filamentous Algae dominated in littoral communities. Spatial and temporal organization of planktonic microbial communities and the quantities of Archaea and Bacteria were investigated using fluorescent in situ hybridization. We have found that the dominant planktonic component is represented by Archaea, or, less frequently, by Bacteria. Various phylogenetic groups (Bacteria, Archaea, Algae, and Cyanobacteria) are nonuniformly distributed. The principal component analysis was used to detect environmental factors that affect microorganism abundance. We found the principal components responsible for 71.1 % of the observed variation. It was demonstrated that two-block partial least squares was a better method than principal component analysis for analysis of the data. We observed general relationships between microbial abundance and water salinity. We have performed the first-ever study of the structure of the microbial communities of eleven saline lakes in the Novosibirsk region along with their physical-chemical parameters of waters. Our study demonstrates that saline lakes in the Novosibirsk region contain a unique microbial communities that may become a prolific source of microorganisms for fundamental and applied studies in various fields of ecology, microbiology, geochemistry, and biotechnology, and deserve further metagenomic investigation.

Net blotch caused by Pyrenophra teres f. teres is a major foliar disease of barley. Infection can result in significant yield losses of susceptible cultivars of up to 40%. Of the two forms of net blotch (P. teres f. teres and P. teres f. maculata), P. teres f. teres (net form of net blotch) is the dominant one in Russia. The goal of the current study was to identify genomic regions associated with seedling resistance to several pathotypes of the net form of net blotch in Siberian spring barley genotypes. For this, a genome-wide association study of a Siberian barley collection, genotyped with 50 K Illumina SNP-chip, was carried out. Seedling resistance of 94 spring barley cultivars and lines to four Pyrenophora teres f. teres isolates (S10.2, K5.1, P3.4.0, and A2.6.0) was investigated. According to the Tekauz rating scale, 25, 21, 14, and 14% of genotypes were highly resistant, and 19, 8, 9, and 16% of genotypes were moderate-resistant to the isolates S10.2, K5.1, P3.4.0, and A2.6.0, respectively. Eleven genotypes (Alag-Erdene, Alan-Bulag, L-259/528, Kedr, Krymchak 55, Omsky golozyorny 2, Omsky 13709, Narymchanin, Pallidum 394, Severny and Viner) were resistant to all studied isolates. Nine additional cultivars (Aley, Barkhatny, Belogorsky, Bezenchuksky 2, Emelya, G-19980, Merit 57, Mestny Primorsky, Slavaynsky) were resistant to 3 of the 4 isolates. The phenotyping and genotyping data were analysed using several statistical models: GLM + Q, GLM + PCA, GLM + PCA + Q, and the MLM + kinship matrix. In total, 40 SNPs in seven genomic regions associated with net blotch resistance were revealed: the region on chromosome 1H between 57.3 and 62.8 cM associated with resistance to 2 isolates (to P3.4.0 at the significant and K5.1 at the suggestive levels), the region on chromosome 6H between 52.6 and 55.4 cM associated with resistance to 3 isolates (to P3.4.0 at the significant and K5.1 and S10.2 at the suggestive levels), three isolate-specific significant regions (P3.4.0-specific regions on chromosome 2H between 71.0 and 74.1 cM and on chromosome 3H between 12.1 and 17.4 cM, and the A2.6.0-specific region on chromosome 3H between 50.9 and 54.8 cM), as well as two additional regions on chromosomes 2H (between 23.2 and 23.8 cM, resistant to S10.2) and 3 (between 135.6 and 137.5 cM resistant to K5.1) with suggestive SNPs, coinciding, however, with known net blotch resistance quantitative trait loci (QTLs) at the same regions. Seven genomic regions on chromosomes 1H, 2H, 3H, and 6H associated with the resistance to four Pyrenophora teres f. teres isolates were identified in a genome-wide association study of a Siberian spring barley panel. One novel isolate-specific locus on chromosome 3 between 12.1 and 17.4 cM was revealed. Other regions identified in the current study coincided with previously known loci conferring resistance to net blotch. The significant SNPs revealed in the current study can be converted to convenient PCR markers for accelerated breeding of resistant barley cultivars.

The hypothalamus has an important role in the onset and maintenance of hypertension and stress responses. Rats with inherited stress-induced arterial hypertension (ISIAH), reproducing the human stress-sensitive hypertensive state with predominant involvement of the neuroendocrine hypothalamic-pituitary-adrenal and sympathoadrenal axes, were used for analysis of the hypothalamus transcriptome. RNA-seq analysis revealed 139 genes differentially expressed in the hypothalami of hypertensive ISIAH and normotensive Wistar Albino Glaxo (WAG) rats. According to the annotation in databases, 18 of the differentially expressed genes (DEGs) were associated with arterial hypertension. The Gene Ontology (GO) functional annotation showed that these genes were related to different biological processes that may contribute to the hypertension development in the ISIAH rats. The most significantly affected processes were the following: regulation of hormone levels, immune system process, regulation of response to stimulus, blood circulation, response to stress, response to hormone stimulus, transport, metabolic processes, and endocrine system development. The most significantly affected metabolic pathways were those associated with the function of the immune system and cell adhesion molecules and the metabolism of retinol and arachidonic acid. Of the top 40 DEGs making the greatest contribution to the interstrain differences, there were 3 genes (Ephx2, Cst3 and Ltbp2) associated with hypertension that were considered to be suitable for further studies as potential targets for the stress-sensitive hypertension therapy. Seven DEGs were found to be common between hypothalamic transcriptomes of ISIAH rats and Schlager mice with established neurogenic hypertension. The results of this study revealed multiple DEGs and possible mechanisms specifying the hypothalamic function in the hypertensive ISIAH rats. These results provide a basis for further investigation of the signalling mechanisms that affect hypothalamic output related to stress-sensitive hypertension development.

Biologists more and more have to deal with objects with non-numeric descriptions: texts (e.g. genetic sequences or even whole genomes), graphs, images, etc. There even could be no variables or descriptions at all when variability of objects is defined by similarity matrix. It is also possible to have too many variables (e.g. a magnitude of millions is reachable in mass spectrometry or genome research). In this case it is necessary to switch to object similarity matrices which drastically reduces dimensionality to hundreds or thousands. It is software developer’s responsibility to keep this use cases in mind and provide means for working with such data instead of shifting the problem to the users. Software should be more convenient for them and allow solving wider range of problems with fairly simple mathematical apparatus. In particular principal component analysis (PCA) is rather popular among biologists. But, the necessity of variables is an illusion. It’s enough to have a matrix of Euclidean distances between objects and apply method of the principal coordinates (PCo) (or multidimensional scaling for dissimilarity matrix, MDS) [1]. In the late 70s of the last century B. Efron proposed generating a set of new samples from the source sample EDF as a model for sample’s general distribution to get confidence estimation. He called it “bootstrap” [2]. For the statistical software developers this primarily means that PCo, MDS, and bootstrap should be implemented. Further, the use of bootstrap results in huge increase of repetitions of data analysis (from hundreds to millions of times) which is impossible to do in interactive mode. Therefore a part of the analysis requiring bootstrap should be written as a script in its entirety. Further user interaction should be eliminated. Obviously this process could be efficiently done in parallel. There are multitude of tools for doing it varying from scripting languages like R or Python to specialized software packages like PAST, CANOCO, Chemostat, STATISTICA, and MATLAB. Researchers who are not versed in software development tend to use tools like PAST, even if they may not cover all their needs, including automating frequently performed tasks. However, automatic analysis is a key element for the upcoming era of bootstrap analysis. We developed a simple and convenient package JACOBI4, which allows researchers without programming experience to automate multidimensional statistical analysis. Package and methods implemented in it can be useful in studies of both medical (gene expression for various diseases) and biological (regularities of molecular sequence variability) data. It goes without saying that the use of JACOBI4 is in no way limited to these examples. The package can be used directly, taking already developed scripts and editing them to fit own needs. Package JACOBI4 is freely available at [w1]. There are also articles available in which JACOBI4 is used to process real world data, as well as supplemental files containing JACOBI4 scripts and data for them.

The adrenals are known as an important link in pathogenesis of arterial hypertensive disease. The study was directed to the adrenal transcriptome analysis in ISIAH rats with stress-sensitive arterial hypertension and predominant involvement in pathogenesis of the hypothalamic-pituitary-adrenal and sympathoadrenal systems. The RNA-Seq approach was used to perform the comparative adrenal transcriptome profiling in hypertensive ISIAH and normotensive WAG rats. Multiple differentially expressed genes (DEGs) related to different biological processes and metabolic pathways were detected. The discussion of the results helped to prioritize the several DEGs as the promising candidates for further studies of the genetic background underlying the stress-sensitive hypertension development in the ISIAH rats. Two of these were transcription factor genes (Nr4a3 and Ppard), which may be related to the predominant activation of the sympathetic-adrenal medullary axis in ISIAH rats. The other genes are known as associated with hypertension and were defined in the current study as DEGs making the most significant contribution to the inter-strain differences. Four of them (Avpr1a, Hsd11b2, Agt, Ephx2) may provoke the hypertension development, and Mpo may contribute to insulin resistance and inflammation in the ISIAH rats. The study strongly highlighted the complex nature of the pathogenesis of stress-sensitive hypertension. The data obtained may be useful for identifying the common molecular determinants in different animal models of arterial hypertension, which may be potentially used as therapeutic targets for pharmacological intervention.